The present disclosure is directed toward a system and method for handling alerts generated by electromechanical devices. The disclosure also relates to a method for categorizing each alert for purposes of attending to the alert.
A managed printing environment typically has goals of maximizing working time for multiple devices, such as printers, within an organization. In such a networked environment, a number of devices can be managed by a remotely located service provider. Generally, in a fleet of heterogeneous devices, the devices are each programmed to fire status messages, in the form of alerts, to a common server located at a customer site. These alerts are composed of codes and description messages that contain valuable information about current resource levels, device health, and the like. The information is pooled at the customer site and relayed to the remote service provider in batches. A conventional method for managing the device alerts involves an operator at the call center who supervises the received information to monitor the status of the printers and to assist in diagnosing and troubleshooting problems. The operator identifies and resolves any issues that are indicated by the alerts. The operator has to sort through the alerts and schedule the responses, based on a level of importance associated with each alert. The importance can vary based on the type of alert and whether the alert relates to a problem that requires immediate attention.
One problem with this approach is that the importance of an alert or pertinent information contained in the alert can be lost amongst an influx of redundant alerts acquired for one device or by identical alerts for multiple devices at one or multiple customer sites. The task of maintaining knowledge of different sets of rules for attending the alerts can be challenging for an operator that is supervising a diverse fleet of devices.
Therefore, filtering systems have been developed for automatically filtering alerts, such as repetitive alerts, from the batch, based on a set of programmed rules. One problem associated with the rule-based filtering approach is that this may not cover all types of alerts encountered by the system. In this case, the filtering process may not be triggered by certain categories of alerts that should be filtered. For example, business rules are flexible and can vary among different fleets of devices. Different sets of customized rules can be associated with certain customer sites. These rule-based systems can therefore require substantial information and experience to formulate. Furthermore, the differences in business rules among fleets and customer sites can be difficult to maintain, which can affect the categorization of alerts for purposes of attending to an alert. Any changes made to rules should be propagated with proper versioning controls and can be difficult to control. Accordingly, the filtering system is continually updated to categorize alerts based on current and changing rule requirements.
There remains a need for a system and a method that enables a categorization scheme to be automatically updated for purposes of filtering, analyzing, and responding to device alerts. A desired system is adapted to analyze each received alert and update a reference list using a categorization model for an alert description that is determined not to have been previously referenced in the model.